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Estimation of COVID-19 cases using robust ratio type estimators: An application of ACS design |
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PP: 1-11 |
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doi:10.18576/jsapl/100101
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Author(s) |
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Rajesh Singh,
Sheela Misra,
Rohan Mishra,
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Abstract |
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| In survey sampling, presence of outliers in the data collected has been one of the biggest concerns. This problem becomes severe when dealing with estimation of communicable diseases. Further, there are several cases where the population under study is hidden clustered or clumped and non-adaptive sampling designs in these cases fail to give a reliable estimate like in case of COVID- 19 during the initial days. To deal with these two problems, in this article we have developed eight robust estimators to estimate the unknown population mean of a rare or hidden clustered population in presence of outliers. In order to assess the performance of the proposed estimators against similar existing robust ratio type estimators, simulation studies have been conducted to estimate the average of COVID-19 cases in the Indian union territory of Andaman & Nicobar Islands and the state of Goa. The results of the study show that the developed estimators perform much better as compared to other similar existing robust ratio type estimators presented in this article.
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